@文章{信息:doi/10.2196/11941，作者=“Pham, Quynh和Graham, Gary和Carrion, Carme和Morita, Plinio P和Seto, Emily和Stinson, Jennifer N和Cafazzo, Joseph A”，标题=“评估消费者移动健康应用程序对慢性疾病的有效参与的分析指标库:范围审查”，期刊=“JMIR移动健康Uhealth”，年=“2019”，月=“Jan”，日=“18”，卷=“7”，数字=“1”，页=“e11941”，关键词=“分析;有效的参与;参与;依从性;日志数据;移动健康;移动应用程序;慢性疾病;背景:目前有各种各样的证据支持移动医疗(mHealth)应用程序改善慢性健康和福祉的雄心。对于这种可变影响的一种解释是，用户并没有按照预期的方式使用应用。 The application of analytics, defined as the use of data to generate new insights, is an emerging approach to study and interpret engagement with mHealth interventions. Objective: This study aimed to consolidate how analytic indicators of engagement have previously been applied across clinical and technological contexts, to inform how they might be optimally applied in future evaluations. Methods: We conducted a scoping review to catalog the range of analytic indicators being used in evaluations of consumer mHealth apps for chronic conditions. We categorized studies according to app structure and application of engagement data and calculated descriptive data for each category. Chi-square and Fisher exact tests of independence were applied to calculate differences between coded variables. Results: A total of 41 studies met our inclusion criteria. The average mHealth evaluation included for review was a two-group pretest-posttest randomized controlled trial of a hybrid-structured app for mental health self-management, had 103 participants, lasted 5 months, did not provide access to health care provider services, measured 3 analytic indicators of engagement, segmented users based on engagement data, applied engagement data for descriptive analyses, and did not report on attrition. Across the reviewed studies, engagement was measured using the following 7 analytic indicators: the number of measures recorded (76{\%}, 31/41), the frequency of interactions logged (73{\%}, 30/41), the number of features accessed (49{\%}, 20/41), the number of log-ins or sessions logged (46{\%}, 19/41), the number of modules or lessons started or completed (29{\%}, 12/41), time spent engaging with the app (27{\%}, 11/41), and the number or content of pages accessed (17{\%}, 7/41). Engagement with unstructured apps was mostly measured by the number of features accessed (8/10, P=.04), and engagement with hybrid apps was mostly measured by the number of measures recorded (21/24, P=.03). A total of 24 studies presented, described, or summarized the data generated from applying analytic indicators to measure engagement. The remaining 17 studies used or planned to use these data to infer a relationship between engagement patterns and intended outcomes. Conclusions: Although researchers measured on average 3 indicators in a single study, the majority reported findings descriptively and did not further investigate how engagement with an app contributed to its impact on health and well-being. Researchers are gaining nuanced insights into engagement but are not yet characterizing effective engagement for improved outcomes. Raising the standard of mHealth app efficacy through measuring analytic indicators of engagement may enable greater confidence in the causal impact of apps on improved chronic health and well-being. ", issn="2291-5222", doi="10.2196/11941", url="http://mhealth.www.mybigtv.com/2019/1/e11941/", url="https://doi.org/10.2196/11941", url="http://www.ncbi.nlm.nih.gov/pubmed/30664463" }